Recommender systems are used for automatically presenting a recommendation relating to a content item, such as a product, a video, a journal, a TV program, a song etc. available online, to a user. As a user is confronted with a large amount of content items that are offered in various databases, he/she can have severe difficulties in finding content items of interest. A recommender system shall facilitate retrieving content items that are of interest for the user and avoid the need to perform complex searches. For instance, international patent application publication WO 2010/122448 A1 describes such a recommender system.
There are different approaches of designing a recommender system. One approach results in a content-based recommender system and another approach in a collaborative-filtering-based recommender system.
A content-based recommender system recommends a content item being represented as multiple features to a user, if this feature representation corresponds to the user's preferences, which are also expressed in terms of these features.
A collaborative-filtering-based recommender system analyses the purchase/viewing/listening history of a comparatively large set of users in order to identify a similarity between content items and a similarity between users. For instance, new content items offered in a content item database that are similar to items that a user likes are recommended to the user. Or, new items that are liked by some users that are similar to a certain user are recommended to the certain user. Usually, a collaborative-filtering-based recommender system does not require specific information on content items themselves other than purchase/viewing/listening information from users.
Besides recommendations that are solely and automatically generated by recommender systems, a user can submit a personal recommendation to one of his contacts. For instance, a presentation of a content item in the internet is often accompanied by a so-called “tell-a-friend” button that a user can click on and thereby submit a link, i.e. a web address, to the representation of the content item and eventually a personal note to one or more of his contacts.
Recommender systems are known that are adapted to promote such personal recommendations or semi-personal recommendations. For instance, a recommender system is known that recommends an item to a user, if one of the contacts of the user has positively rated the item. Such semi-personal recommendation can also be generated, if the contact of the user has only implicitly expressed that he/she likes a content item. For instance, a recommender system of the user recognises that the user likes or dislikes a certain content item and forwards this recognition in the form of a recommendation to all contacts of the user, thereby informing all contacts of the user that the user likes/dislikes a certain content item. The recognition, whether the user likes/dislikes a certain content item can also be based on an explicit rating the user has submitted in relation to this certain content item.
Another possible recommendation mechanism is that a certain content item is recommended to the user, whenever a sufficiently large fraction of contacts of the user has rated the content item sufficiently high. Again, such recommendation of an item to the user can be based on explicit ratings that are given to the content item by the user's contacts, but it can also be based on estimated like-degrees that a recommender system has determined for the contacts of the user.
A user participating in a computer-implemented social network, such as Facebook, MySpace, LinkedIn, Xing and so forth can have a large amount of associated contacts. Thus, the number of automatically generated semi-personal recommendations can quickly increase such that a main advantage of semi-personal recommendations, namely that recommendations exhibiting some personal character are assumed to be trusted more than recommendations solely made by an algorithm, can be rendered mute.